Model predictive control (MPC) algorithms with state-space process modelling, both linear and nonlinear, and state estimation methods for this algorithms are the subject of this paper. The considerations are under realistic assumption that processes are under influence of external disturbances and their models are not precise. This leads in most cases to errors in state estimation and, further, may lead to errors in feedback control. Attention has been paid to this problem in recent years. Two approaches are now available, an earlier one with additional disturbance state modelling and process-and-disturbance state estimation and a more recently proposed approach with different way of disturbance modelling and estimation of the process state only. The main aim of this paper is to provide a comprehensive comparison of the two mentioned approaches, including also discussion of available state estimation algorithms. To the best knowledge of the authors, there is still a lack of clear understanding of the differences between the mentioned approaches, in particular from practical point of view. After short presentation of the two methods and analysis of their theoretical aspects, a comprehensive comparative analysis is provided on a nonlinear example of the polymerization reactor.

具有状态空间过程建模（线性和非线性）的模型预测控制（MPC）算法以及该算法的状态估计方法是本文的主题。这些考虑是在现实的假设下进行的，即过程受外部干扰的影响，并且其模型不精确。在大多数情况下，这会导致状态估计错误，并且进一步可能会导致反馈控制错误。近年来，已经对该问题给予了关注。现在有两种方法，一种是早期的方法，具有附加的干扰状态建模和过程与干扰状态估计，另一种是最近的方法，仅具有不同的干扰模型和过程状态估计方法。本文的主要目的是对上述两种方法进行全面比较，还包括对可用状态估计算法的讨论。就作者所知，尤其是从实际角度出发，仍然缺乏对上述方法之间差异的清楚理解。在简短介绍了这两种方法并对其理论方面进行了分析之后，对聚合反应器的一个非线性实例进行了全面的比较分析。